5 accepted papers
The advent of increasingly available patient health data has created opportunities for critical care medicine to embrace modern data-driven decision support. However, existing methods, such as rule-based scoring systems or machine learning-based classification models, often focus on uni-dimensional
Sepsis progression varies widely among intensive care unit (ICU) patients, making early and accurate trajectory prediction crucial. Traditional models focus on static assessments or binary outcomes, limiting their clinical utility. This study considers clinical trajectories of patients with sepsis f
The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside prov
Hospitals worldwide share common challenges in finding the optimal number of patient beds in advance, which is referred to as the bed capacity management problem. Effective management cannot be achieved without an appropriate understanding of the stochastic nature of patient demands. In light of the
Closed-loop production lines with constant number of carriers are widely encountered in manufacturing systems. In this letter, we introduce an iteration method to study the key performance indicator (KPI) of closed-loop production lines with geometric reliability machines. First, analysis of two-mac